We study how and where tropical storms, i.e., tropical cyclogenesis, begin in the Western North Pacific. Using many years of global weather data and a modern pattern-recognition method, i.e., deep learning,  we built a model that learns signals that come before storm formation and maps when and where formation is likely. It reproduces known seasonal and regional patterns and identifies key environmental cues. These results can support better risk planning and help refine climate projections.